Foundation Article

Summary and Commentary

Early studies using diagnostic interviews to extract lifetime histories of
depression in diabetic subjects indicated that first episodes of major
depression typically preceded the diagnosis of type 2 diabetes. A 13-year
follow-up of participants in the Epidemiologic Catchment Area (ECA) by Eaton
et al. provided some of the first longitudinal evidence that depression might
be a risk factor for the development of diabetes. The ECA, a landmark study in
psychiatric epidemiology, documented the prevalence of major psychiatric
disorders in the United States. Its primary findings were summarized in
Psychiatric Disorders in America by Robins and
Regier.7

A structured interview, the Diagnostic Interview Schedule (DIS), was
developed for the ECA study. It enabled trained lay interviewers to assess the
symptoms that were used to diagnose psychiatric disorders such as major
depression according to the American Psychiatric Association's official
criteria. The DIS determines whether the subject has ever met the criteria for
each disorder and, if so, when the most recent episode occurred. The subjects
in the analyses by Eaton et al. were classified according to whether they had
ever had a major depressive episode before their ECA interview, regardless of
whether they were depressed at the time of the interview.

Utilizing the lifetime diagnosis of depression as a predictor of diabetes
made it especially important to take age at enrollment into account. Older
subjects had had more time to develop depression, diabetes, or both compared
to younger subjects. However, individuals who already had diabetes at
enrollment in the ECA were excluded from the analysis. Thus, age at enrollment
influenced both inclusion in the sample and the participants' likelihood of
ever having been depressed.

Baltimore, Md., was one of several ECA sites; it enrolled 3,481 adult
subjects (> 18 years old). Selected items from the National Center for
Health Statistics Health Interview Survey (HIS) were used to determine whether
each subject had or was being treated for diabetes. Subjects who affirmed any
of these items were excluded from the analysis.

The follow-up data were collected from 1993 through 1996. By then, 847
members of the Baltimore cohort had died, according to a National Death Index
search. A total of 1,897 (∼ 72%) of the 2,652 survivors were interviewed,
1,715 of whom had denied diabetes in 1981. The authors acknowledged that some
of these might have had undiagnosed diabetes at the time, but since the study
protocol did not include a medical examination, it was impossible to identify
undiagnosed cases. The follow-up interview included a more detailed set of
questions about diabetes and its treatment than was utilized in 1981.
Excluding individuals who experienced only gestational diabetes in the
interim, 89 new cases of diabetes were identified among the 1,715 who could
have been at risk in 1981. This represents a cumulative incidence of about 5%.
Like the initial assessment, an unknown number of undiagnosed cases might have
been missed by the follow-up interview.

Seventy-six of the subjects who had complete diabetes data on the follow-up
had met the lifetime major depression in 1981. Six (8%) reported diabetes at
follow-up, compared with 80 (5%) of the 1,604 subjects who had never been
depressed as of 1981. This represents a relative risk (RR) of 1.6 (95% CI,
0.7–3.5). Thus, the univariate relationship was in the predicted
direction, but it was not statistically significant. Major depression was also
not a significant predictor of diabetes in a logistic regression model,
despite its odds ratio (OR) of 2.2. This model showed age to be a significant
risk factor, with ORs of 3.2 and 4.2 for the 45- to 64-year-old and the ≥
65-year-old groups, respectively, compared to those between 18 and 29 years of
age. Body mass index (BMI) was also significant (OR = 1.1), but sex and race
were not. Several other forms of depression were explored as potential
predictors, as were several anxiety disorders and alcohol dependence, but none
was significant.

The report concluded that major depression predicts the development of
diabetes. However, the results did not support this conclusion. A significant
depression effect might have been found if there had been more new cases of
diabetes to model, and consequently greater statistical power to detect an
effect, but there were only 86 new cases. The effect was not statistically
significant, and therefore the study did not provide compelling evidence that
it exists in the population from which the sample was drawn. Nevertheless, the
findings were certainly intriguing, and they inspired other investigators to
look for evidence that depression is an independent risk factor for
diabetes.

Since the publication of this provocative report, several other studies
have yielded evidence that depression might increase the risk of developing
diabetes. Kawakami et al.8 conducted an 8-year prospective study of
2,764 male employees of a Japanese company. Subjects were excluded from the
analysis if they had diabetes at entry into the study, according to the
company's medical records and interviews by research nurses. The Zung
Depression Scale was used to measure the severity of depressive symptoms. This
is quite different from the approach taken by Eaton et al., in that the Zung
is a self-report questionnaire rather than a structured interview, and a high
Zung score does not necessarily mean that the subject meets the criteria for
major depression. Furthermore, questionnaires such as the Zung assess current
symptoms of depression rather than the individual's lifetime history of major
depressive disorder.

New cases of diabetes were detected on an annual medical examination that
included a fasting glucose test. Over the 8-year follow-up, 43 participants
developed type 2 diabetes. Moderate or severe depression (Zung score ≥ 48
was a significant univariate predictor of diabetes (hazard ratio [HR] = 2.3;
95% CI, 1.1–5.1). In contrast, the effect of mild depression was not
significant. In a Cox proportional hazards regression analysis, moderate to
severe depression remained an independent predictor of time to onset of
diabetes (HR = 2.3) after adjusting for age, BMI, smoking, alcohol
consumption, physical activity, medical comorbidity, and family history of
diabetes. The 17 new cases who were detected in the first 4 years of the
follow-up were excluded from a secondary analysis in order to address the
possibility that they had had undiagnosed diabetes on the initial assessment.
The covariate-adjusted effect of depression (HR = 2.8) was even stronger in
this analysis than in the primary model.

Carnethon et al.,9 in another study, used data from the First
National Health and Nutrition Examination Survey (NHANES I) and the National
Health and Nutrition Examination Epidemiologic Follow-Up Survey (NHEFS) to
determine whether the effect of depression on the onset of type 2 diabetes is
mediated by established risk factors for diabetes. Their sample included 2,858
men and 3,332 women. Diabetes was documented by medical records and/or
self-report, and current depression was measured by the four-item Depression
subscale of the General Well-Being Survey. Over an average of > 15 years of
follow-up, 6% of the participants developed type 2 diabetes. The incidence of
diabetes was higher among those with high depression scores (7.3/1,000
person-years) than among those with intermediate or low scores (3.4/1,000
person-years and 3.6/1,000 person-years, respectively). The association
between depression and diabetes was significant among individuals with less
than a high school education, but not among better-educated respondents. The
risk of developing diabetes was about three times higher among depressed than
nondepressed individuals in the less educated subgroup. In the entire cohort,
the covariate-adjusted risk of developing diabetes increased 4% per standard
deviation increase in depression. About 31% of the association was explained
by differences in BMI and 6% by behaviors including smoking, alcohol use, and
physical inactivity.

Arroyo et al.10 analyzed data from a 4-year follow-up of 72,178
female participants in the Nurses Health Study. They did not have a measure of
depression per se, but they did have the five-item Mental Health Index (MHI-5)
from the Short-Form 36 quality of life questionnaire. Low scores on the MHI-5
reflect high current levels of depression, anxiety, and/or closely related
forms of distress. For the purposes of this study, individuals with an MHI-5
score ≥ 2 were classified as having current depressive symptoms on the
initial evaluation. Diabetes was assessed by a detailed biennial questionnaire
covering recent symptoms, diagnostic tests, and treatments for diabetes.

During the follow-up period, 973 new cases of type 2 diabetes were
reported. Logistic regression was used to adjust for age, smoking, BMI,
physical inactivity, alcohol use, menopausal status, parental history of
diabetes, and other factors. The RR of developing diabetes for individuals
with depressive symptoms in the fully adjusted model was 1.2 (95% CI,
1.0–1.5, P = 0.05). The interpretation of this result depends
to some extent on whether one views factors such as BMI and physical
inactivity as confounders or as mediators of the effect of depression on
diabetes. The effect was stronger when adjusting only for age and BMI (RR =
1.4; 95% CI, 1.1–1.7, P = 0.003) and stronger still when
adjusting only for age (RR = 1.6; 95% CI, 1.3–1.9, P <
0.0001).

Finally, Golden et al.11 used data from 11,615 men and women in
the Atherosclerosis Risk in Communities (ARIC) study to analyze the effects of
“vital exhaustion” on the development of type 2 diabetes. The
symptoms of vital exhaustion overlap with those of depression and include
symptoms such as fatigue, hopelessness, loss of libido, irritability, crying,
and dejection. They were measured by Appel's Vital Exhaustion Scale, and
diabetes was documented by medical examinations conducted every 3 years during
a 6-year follow-up.

There were 721 new cases of type 2 diabetes, corresponding to an incidence
rate of 12.4/1,000 person-years of follow-up. The age-, sex-, and raceadjusted
incidence was highest (19.1%) among participants in the highest quartile of
vital exhaustion, and it was significantly different from the incidence among
those in the lowest quartile (P < 0.001). In a Cox proportional
hazards regression model adjusting for age, sex, race, education, and ARIC
study center, vital exhaustion quartile was an independent predictor of
developing type 2 diabetes (HR for the fourth vs. the first quartile = 1.6;
95% CI, 1.3–2.0). In a series of additional models, the effect survived
adjustment for metabolic covariates (HR = 1.4, P = 0.007), lifestyle
covariates (HR = 1.5, P = 0.0005), and both sets of covariates (HR =
1.3, P = 0.04). The HR exceeded 1.0 but was not significant when
adjusted for lifestyle covariates and BMI (HR = 1.3, P = 0.06).

In short, these studies provide converging evidence that depression is a
risk factor for the development of type 2 diabetes. The instruments used to
measure depression differed from one study to the next, and the study by Eaton
et al. was the only one to study the effects of major depressive disorder
rather than depressive symptoms measured by a self-report questionnaire. The
rigor with which diabetes was assessed also differed among the studies. There
is still a need for a prospective study in which depression, diabetes, and
potential confounders and mediators of the relationship between them are
evaluated with comparable rigor in a large cohort. Nevertheless, the existing
studies provide reasonably persuasive evidence that depression increases the
risk of developing type 2 diabetes and raise the question whether depression
treatment might delay or prevent its onset.

Footnotes

Kenneth E. Freedland, PhD, is a professor of psychiatry at Washington
University School of Medicine in St. Louis, Mo.